Contemporary approaches to psychometrics: item response theory and diagnostic classification models / Enfoques contemporáneos sobre psicometría: los modelos de la teoría de respuesta al ítem y los modelos de clasificación de diagnósticos

Bo Hu, L. Qin, Meghan Sullivan, Jonathan Templin
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引用次数: 4

Abstract

Abstract Evaluating test scores is an essential process, critical in both educational research and practice. To scientifically understand and utilize test scores, educators and researchers need to choose appropriate psychometric models to analyse and interpret assessment data. In this paper, we discuss two classes of psychometric models that have been widely used in educational measurement: item response theory (IRT) models and diagnostic classification models (DCMs). Specifically, the IRT discussion focuses on producing scores on a numerical continuum using the two-parameter logistic model. We then discuss methods for producing scores based on ordinal classifications with DCMs and compare and contrast such scores with those from IRT models. In addition, through step-by-step examples, we demonstrate how to obtain estimates from and interpret results from each model we present. We conclude the paper with considerations in and suggestions for choosing an appropriate psychometric model.
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当代心理测量学方法:项目反应理论和诊断分类模型/当代心理测量学方法:项目反应理论模型和诊断分类模型
评价考试成绩是一个重要的过程,在教育研究和实践中都是至关重要的。为了科学地理解和利用考试成绩,教育工作者和研究人员需要选择合适的心理测量模型来分析和解释评估数据。本文讨论了在教育测量中广泛应用的两类心理测量模型:项目反应理论模型(IRT)和诊断分类模型(dcm)。具体来说,IRT讨论的重点是使用双参数逻辑模型在数值连续体上产生分数。然后,我们讨论了基于dcm的有序分类产生分数的方法,并将这些分数与IRT模型的分数进行了比较和对比。此外,通过一步一步的示例,我们演示了如何从我们呈现的每个模型中获得估计并解释结果。最后,我们提出了选择合适的心理测量模型的考虑和建议。
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